View source: R/plot.SuperLearner.R
plot.SuperLearner | R Documentation |
Does not include SuperLearner or Discrete SL results as that requires CV.SuperLearner to estimate the standard errors.
## S3 method for class 'SuperLearner'
plot(x, y = x$Y, constant = qnorm(0.975), sort = T, ...)
x |
SuperLearner result object |
y |
Outcome vector |
constant |
Multiplier of the standard error for confidence interval construction. |
sort |
If TRUE re-orders the results by risk estimate. |
... |
Any remaining arguments (unused). |
plot object; print to display.
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
SuperLearner
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1)
sl = SuperLearner(Boston$medv, subset(Boston, select = -medv),
family = gaussian(),
SL.library = c("SL.mean", "SL.glm"))
sl
plot(sl, y = Boston$chas)
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